Book Image

Mastering Hadoop 3

By : Chanchal Singh, Manish Kumar
Book Image

Mastering Hadoop 3

By: Chanchal Singh, Manish Kumar

Overview of this book

Apache Hadoop is one of the most popular big data solutions for distributed storage and for processing large chunks of data. With Hadoop 3, Apache promises to provide a high-performance, more fault-tolerant, and highly efficient big data processing platform, with a focus on improved scalability and increased efficiency. With this guide, you’ll understand advanced concepts of the Hadoop ecosystem tool. You’ll learn how Hadoop works internally, study advanced concepts of different ecosystem tools, discover solutions to real-world use cases, and understand how to secure your cluster. It will then walk you through HDFS, YARN, MapReduce, and Hadoop 3 concepts. You’ll be able to address common challenges like using Kafka efficiently, designing low latency, reliable message delivery Kafka systems, and handling high data volumes. As you advance, you’ll discover how to address major challenges when building an enterprise-grade messaging system, and how to use different stream processing systems along with Kafka to fulfil your enterprise goals. By the end of this book, you’ll have a complete understanding of how components in the Hadoop ecosystem are effectively integrated to implement a fast and reliable data pipeline, and you’ll be equipped to tackle a range of real-world problems in data pipelines.
Table of Contents (23 chapters)
Title Page
Dedication
About Packt
Foreword
Contributors
Preface
Index

Chapter 8. Designing Applications in Hadoop

In the previous chapter, we talked about multiple widely used components in data processing. We focused on a batch processing framework, Apache Pig, and talked about its architecture. We discussed a distributed columnar store database, HBase, and also covered the distributed messaging system, Kafka, which gives you ability to store and persist real-time events. Apache Flume was also the focus of the last chapter, which can help in pulling some real-time logs for further processing.  In this chapter, we will talk about some of the design considerations for application processing semantic. The following will be some of the focus points of this chapter:

  • Different file formats available
  • Advantage of using compression codecs 
  • Best data ingestion practices
  • Design consideration for applications
  • Data governance and its importance